In [1]}, Denton et al. used generated counterfactual examples to discover and assess unknown biases. By supposing that a generative model was available, they generated counterfactual examples given a set of interpretable attributes, and tested the performance of a trained classifier. A significant drop of the classifier performance was then considered as a good indicator that a specific attribute used to generate the counterfactual example was highly influential, and could therefore reveal an unintended bias. In much the same manner, Li et al.  [2]} proposed to discover unknown biased factors in a classifier by generating factor traversals with generative models and a special hyperplane optimization. In this method, the classifier and the generative models have their weights fixed, i.e. they are already trained, so only the hyperplane is optimized thanks to the model outputs. Thus, images traversals are generated with more and more relevance with respect to orthogonal dimensions and largest variations on the classification scores. We can also highlight the work of Paul et al. [3]} which expand the scope of fairness from focusing solely on demographic factors to more general factors by using generative models that discover them.
